Triple

T1017299
Position Surface form Disambiguated ID Type / Status
Subject Stephen I of Hungary E21958 entity
Predicate capitalDuringReign P9119 FINISHED
Object Esztergom E175407 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Esztergom | Statement: [Stephen I of Hungary, capitalDuringReign, Esztergom]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Esztergom
Context triple: [Stephen I of Hungary, capitalDuringReign, Esztergom]
  • A. Esztergom chosen
    Esztergom is a historic Hungarian city on the Danube River that served as an early royal capital and remains a major religious and cultural center.
  • B. Székesfehérvár
    Székesfehérvár is a historic city in central Hungary that served as a medieval royal seat and coronation site for Hungarian kings.
  • C. Veszprém
    Veszprém is a historic city in western Hungary known for its medieval castle district and role as a regional cultural and administrative center.
  • D. Pozsony
    Pozsony is the historical Hungarian name for the city now known as Bratislava, the capital of Slovakia.
  • E. Siófok
    Siófok is a popular resort town on the southern shore of Lake Balaton in Hungary, known for its beaches and vibrant summer tourism.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69a493c68e24819080ed0ee8bcfd5ce0 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b7c4d488819081d8214ba0a22fe5 completed March 1, 2026, 10:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad4001bcd4819088f59e31e3ed79cf completed March 8, 2026, 9:23 a.m.
Created at: March 1, 2026, 7:41 p.m.